
Firstly, it is critical to obtain robust algorithms that generalize to the test domain with a certified level of performance, and that do not depend on specific working conditions or types of sensors to work as intended. The property which is sought is the robustness of the algorithm.

Secondly, the second goal is to learn representations independent of non-informative variables that can correlate with actual predictive information and play the role of confounding variables. The relationship between Fairness and these representations constitutes an open challenge. In many cases, representations are affected by spurious correlations between subjects and backgrounds (Waterbirds, Benchmarking Attribution Methods), or gender and occupation (Athletes and health professionals, political person) that influence too much the selection of the features, and hence, the algorithmic decision. One way of studying it, is through disentangled representations [1]}: by isolating each factor of variation into a specific dimension of the latent space, it is possible to ensure the independence with respect to sensitive variables.

